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. 2017 Feb;11(2):309-314.
doi: 10.1038/ismej.2016.132. Epub 2016 Nov 8.

Critical decisions in metaproteomics: achieving high confidence protein annotations in a sea of unknowns

Emma Timmins-Schiffman  1 , Damon H May  1 , Molly Mikan  2 , Michael Riffle  3 , Chris Frazar  1 , H R Harvey  2 , William S Noble  1   4 , Brook L Nunn  1
Affiliations

Critical decisions in metaproteomics: achieving high confidence protein annotations in a sea of unknowns

Emma Timmins-Schiffman et al. ISME J. 2017 Feb.
No abstract available

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Figures

Figure 1
Figure 1
Schematic of the workflow for the database searches of the metaproteomics samples. The width of the cylinders depicting each database are scaled to the number of unique tryptic peptides in each database (Supplementary Information 4).
Figure 2
Figure 2
A heat map depicting the amount of agreement of taxonomic assignments at the phylum level derived from inferred proteins across searched databases. For each phylum, a colored box represents the number of proteins (log(x+1)-transformed) associated with that phylum for each set of search results (red=highly abundant phylum; blue=low/non-existent phylum). The results are ordered by phylum abundance in the site/time-specific metagenome search results.
Figure 3
Figure 3
Depiction of the recommended ‘best practices in metaproteomics' workflow. The ocean circles represent data derived from the same sample. (1) Selection of an accurate and efficient database is followed by (2) finding the consensus BLAST hit among the group of best hits, and (3) re-searching the data against more sequences to achieve greater metaproteome coverage using a robust multi-step or iterative algorithm.
See this image and copyright information in PMC

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